Calibration time reduction through local activities estimation in motor imagery-based brain-computer interfaces

Abstract

Objective: One of the main limitations for the practical use of brain-computer interfaces (BCI) is the calibration phase. Several methods have been suggested for the truncating of this undesirable time, including various variants of the popular CSP method. In this study, we cope with the problem, using local activities estimation (LAE).

Approach: LAE is a spatial filtering technique that uses the EEG data of all electrodes along with their position information to emphasize the local activities. After spatial filtering by LAE, a few electrodes are selected based on physiological information. Then the features are extracted from the signal using FFT and classified by the support vector machine. In this study, the LAE is compared with CSP, RCSP, FBCSP and FBRCSP in two different electrode configurations of 118 and 64-channel.

Main results: The LAE outperforms CSP-based methods in all experiments using the different number of training samples. The LAE method also obtains an average classification accuracy of 84% even with a calibration time of fewer than 2 min Significance: Unlike CSP-based methods, the LAE does not use the covariance matrix, and also uses a priori physiological information. Therefore, LAE can significantly reduce the calibration time while maintaining proper accuracy. It works well even with a few training samples.